import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("D://DeepLearning//MNIST", one_hot=True)

xs=tf.placeholder(tf.float32,[None,784],name='x_input')
ys=tf.placeholder(tf.float32,[None,10],name='y_input')
keep_prob=tf.placeholder(tf.float32)

x_image=tf.reshape(xs,[-1,28,28,1],name='input_image')

sess=tf.Session()

def compute_accuracy(v_xs,v_ys):
    global prediction
    y_pre=sess.run(prediction,feed_dict={xs:v_xs,keep_prob:1})
    correct_prediction=tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
    accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result=sess.run(accuracy,feed_dict={xs:v_xs})
    return result

def weight_variable(shape):
    initial=tf.truncated_normal(shape=shape,stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial=tf.constant(0.1,shape=shape,dtype=tf.float32)
    return tf.Variable(initial)

def conv2d(x,W):
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')

def max_pool_2_2(x):
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
#conv1 layer
W_conv1=weight_variable([5,5,1,32])
b_conv1=bias_variable([32])
h_conv1=conv2d(x_image,W_conv1)+b_conv1
h_conv1=tf.nn.relu(h_conv1)
h_pool1=max_pool_2_2(h_conv1)
#conv2 layer
W_conv2=weight_variable([5,5,32,64])
b_conv2=bias_variable([64])
h_conv2=conv2d(h_pool1,W_conv2)+b_conv2
h_conv2=tf.nn.relu(h_conv2)
h_pool2=max_pool_2_2(h_conv2)

#func1 layer
W_fc1=weight_variable([7*7*64,1024])
b_fc1=bias_variable([1024])
h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64])
h_fc1=tf.matmul(h_pool2_flat,W_fc1)+b_fc1
h_fc1=tf.nn.relu(h_fc1)
h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob=keep_prob)

#func2 layer
W_fc2=weight_variable([1024,10])
b_fc2=bias_variable([10])
prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)

#loss function
cross_entropy=ys*tf.log(prediction)
cross_entropy=-tf.reduce_sum(cross_entropy,reduction_indices=[1])
cross_entropy=tf.reduce_mean(cross_entropy)
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

sess.run(tf.initialize_all_variables())
for i in range(1000):
    batch_xs,batch_ys=mnist.train.next_batch(100)
    sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys,keep_prob:0.5})
    if i% 50==0:
        print(compute_accuracy(mnist.test.images,mnist.test.labels))
sess.close()